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BackOps is building the AI resolution layer for companies in the physical economy—logistics, manufacturing, retail, and related industries. The company's platform, Relay, deploys AI operators that automate operational workflows by understanding problems, reconstructing context across fragmented systems (ERPs, WMSs, TMSs, carrier portals, email, spreadsheets), determining next actions, executing via tools, handling exceptions, and carrying work through to resolution. BackOps recently raised a $42M Series B led by Insight Partners.
As Product Manager, AI Agents, you will define what it means for an AI operator to excel at operational work. This is not a role adding AI features to existing SaaS; AI is the product itself. You'll own the product systems and experiences that make agents intelligent, trustworthy, controllable, and increasingly autonomous.
Key Ownership Areas:
**Agent Lifecycle & Strategy**: Define how BackOps agents understand work, reason about it, take action, handle ambiguity, recover from failure, learn from outcomes, and collaborate with humans. You'll answer foundational questions: What context does an agent need? Which workflow parts should be deterministic vs. agentic? When should agents act autonomously vs. request approval? How should agents interpret SOPs, business rules, and unstructured information? How do we make agent behavior configurable without requiring customers to become prompt engineers?
**Workflow Transformation**: Spend time with customers and operators understanding how work actually happens—including workarounds, exceptions, judgment calls, and tribal knowledge. Determine what the best AI-native version of that workflow should be. Work across order management, shipment exceptions, claims, billing, document processing, scheduling, vendor coordination, and new categories. The objective is building a better process, not reproducing the manual one step-for-step.
**Production AI Quality**: For AI products, you must define what good looks like and build systems for continuously measuring it. Own product strategy for agent quality, partnering with engineering to develop evaluation frameworks, golden datasets, test cases, simulation and regression testing, human evaluation, LLM-based evaluation, production monitoring, failure taxonomies, and feedback systems that turn real-world outcomes into better agent behavior.
**Human + AI Collaboration**: Define boundaries between automatic execution, approval-required tasks, and human judgment. Build experiences that help operators understand what Relay is doing, trust it, correct it when necessary, and progressively hand over more work as confidence grows.
**Intelligence Compounding**: Determine how BackOps turns operational data (exceptions, interventions, decisions, failures, resolutions) into product advantage, improving agent quality, workflow design, and customer outcomes over time.
**Outcome Measurement**: Define and own metrics including end-to-end resolution rate, no-touch/autonomous resolution rate, task accuracy, human intervention rate, time to resolution, failure and recovery patterns, customer adoption and trust, and operational impact.
Requirements:
- 6+ years of relevant product-building experience with meaningful ownership at Senior or Staff level
- Shipped AI/ML, LLM, agentic, automation, or other technically sophisticated products into production; owned the outcome, not simply added an AI feature to an existing roadmap
- Deep understanding of non-deterministic product challenges: evaluation, quality measurement, grounding, tool use, context, failure modes, guardrails, human escalation, continuous improvement
- Exceptional product instincts; can take complicated workflows and make resulting UX feel obvious
- Technical fluency to work deeply with engineers and AI practitioners without outsourcing hard product decisions
- Built zero-to-one products or substantially rethought how an existing category should work
- Stay extraordinarily close to users; comfortable observing messy real-world processes rather than relying exclusively on feature requests
- Can distinguish between impressive AI capability and a product customers will trust with real work
- Move quickly, think rigorously, energized by product surfaces where many right answers haven't been discovered
Particularly Compelling Background:
- Built core products at a breakout AI company or exceptional, high-growth B2B software company
- Directly owned agentic systems, LLM applications, AI workflow products, AI-native interfaces, or evaluation systems
- Built products where reliability, accuracy, explainability, or trust had meaningful real-world consequences
- Worked in logistics, manufacturing, commerce, construction, healthcare, field services, or operationally complex industries
- Built for non-technical users with little patience for software requiring workflow changes
- Developed products where software interacts with both digital systems and real-world operational processes
Note: You don't need to arrive knowing supply chain, but you should be the kind of product builder who can become dangerous in a complicated domain very quickly.
